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Stijn Viaene

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Lieselot Danneels & Stijn Viaene, 2022. "Identifying Digital Transformation Paradoxes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 483-500, August.

    Cited by:

    1. Henner Gimpel & Robert Laubacher & Oliver Meindl & Moritz Wöhl & Luca Dombetzki, 2024. "Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing," Group Decision and Negotiation, Springer, vol. 33(5), pages 1301-1322, October.
    2. Qi, Yudong & Han, Minmin & Zhang, Chao, 2024. "The Synergistic Effects of Digital Technology Application and ESG Performance on Corporate Performance," Finance Research Letters, Elsevier, vol. 61(C).

  2. Philip Rogiers & Stijn Viaene & Jan Leysen, 2020. "The digital future of internal staffing: A vision for transformational electronic human resource management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 182-196, October.

    Cited by:

    1. Nur Muhammaditya & Sudarsono Hardjosoekarto & One Herwantoko & Yulia Gita Fany & Mahari Is Subangun, 2022. "Institutional Divergence of Digital Item Bank Management in Bureaucratic Hybridization: An Application of SSM Based Multi-Method," Systemic Practice and Action Research, Springer, vol. 35(4), pages 527-553, August.

  3. Mertens, Willem & Recker, Jan & Kummer, Tyge-F. & Kohlborn, Thomas & Viaene, Stijn, 2016. "Constructive deviance as a driver for performance in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 193-203.

    Cited by:

    1. Ulrich Matthias König & Alexander Linhart & Maximilian Röglinger, 2019. "Why do business processes deviate? Results from a Delphi study," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 425-453, December.
    2. Lagzi, Mohammad Dana & sajadi, Seyed Mojtaba & Taghizadeh-Yazdi, Mohammadreza, 2024. "A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    3. Tekmen, Esra Erenler & Kaptangil, Kerem, 2022. "The Determinants of Constructive Deviant Behaviour of Frontline Tourism Employees: An Exploration with Perceived Supervisory Support and Intrinsic Motivation," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 10(1), pages 58-74.
    4. Thomas Grisold & Christian Janiesch & Maximilian Röglinger & Moe Thandar Wynn, 2024. "Managing Dynamics in and Around Business Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(5), pages 533-540, October.
    5. Mortimer, Gary & Fazal-e-Hasan, Syed Muhammad & Strebel, Judi, 2021. "Examining the consequences of customer-oriented deviance in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    6. Yanyan Lv & Xiaoguang Liu & Guomin Li & Yongrok Choi, 2020. "Managerial Pro-Social Rule Breaking in the Chinese Organizational Context: Conceptualization, Scale Development, and Double-Edged Sword Effect on Employees’ Sustainable Organizational Identification," Sustainability, MDPI, vol. 12(17), pages 1-23, August.
    7. Hao Ji & Jin Yan, 2023. "Why does counterproductive work behavior lead to pro-social rule breaking? The roles of impression management motives and leader-liking," Asia Pacific Journal of Management, Springer, vol. 40(4), pages 1323-1339, December.
    8. Syed Muhammad Fazal-e-Hasan & Gary Mortimer & Ian Lings & Harjit Sekhon & Kerry Howell, 2021. "Managing Relationships: Insights from a Student Gratitude Model," Research in Higher Education, Springer;Association for Institutional Research, vol. 62(1), pages 98-119, February.
    9. Tierney, Kieran D. & Oswald Karpen, Ingo & Westberg, Kate, 2022. "Brand meaning and institutional work: The light and dark sides of service employee practices," Journal of Business Research, Elsevier, vol. 151(C), pages 244-256.
    10. Mertens, Willem & Recker, Jan, 2020. "How store managers can empower their teams to engage in constructive deviance: Theory development through a multiple case study," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    11. Mertens, Willem & Recker, Jan, 2020. "Can constructive deviance be empowered? A multi-level field study in Australian supermarkets," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).

  4. Viaene, Stijn & Danneels, Lieselot, 2015. "Driving digital: welcome to the ExConomy," Journal of Financial Perspectives, EY Global FS Institute, vol. 3(3), pages 182-187.

    Cited by:

    1. Lieselot Danneels & Stijn Viaene, 2022. "Identifying Digital Transformation Paradoxes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 483-500, August.

  5. Viaene, Stijn & Ayuso, Mercedes & Guillen, Montserrat & Van Gheel, Dirk & Dedene, Guido, 2007. "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, Elsevier, vol. 176(1), pages 565-583, January.

    Cited by:

    1. Mercedes Ayuso(universitat de Barcelona) & Miguel Santolino(Universitat de Barcelona), 2009. "Individual prediction of automobile bodily injury claims liabilities," Working Papers in Economics 220, Universitat de Barcelona. Espai de Recerca en Economia.
    2. Wang, Xiaofang & Zhuang, Jun, 2011. "Balancing congestion and security in the presence of strategic applicants with private information," European Journal of Operational Research, Elsevier, vol. 212(1), pages 100-111, July.
    3. Haupt, Johannes & Bender, Benedict & Fabian, Benjamin & Lessmann, Stefan, 2018. "Robust identification of email tracking: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 271(1), pages 341-356.
    4. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    5. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    6. Ming-Jyh Wang & Chieh-Hua Wen & Lawrence W Lan, 2010. "Modelling Different Types of Bundled Automobile Insurance Choice Behaviour: The Case of Taiwan*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 35(2), pages 290-308, April.
    7. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
    8. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," MPRA Paper 75726, University Library of Munich, Germany, revised Dec 2013.
    9. Samuel Antwi & Xicang Zhao, 2012. "National Health Insurance; Claims; Logistic Regression;Odds Ratio; Ghana," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 2(7), pages 139-147, December.
    10. Yankol-Schalck, Meryem, 2022. "The value of cross-data set analysis for automobile insurance fraud detection," Research in International Business and Finance, Elsevier, vol. 63(C).
    11. Mercedes Ayuso & Miguel Santolino, 2012. "Forecasting the Maximum Compensation Offer in the Automobile BI Claims Negotiation Process," Group Decision and Negotiation, Springer, vol. 21(5), pages 663-676, September.
    12. Samuel Antwi & Xicang Zhao, 2012. "National Health Insurance; Claims; Logistic Regression;Odds Ratio; Ghana," International Journal of Business and Social Research, LAR Center Press, vol. 2(7), pages 139-147, December.
    13. Dwi Widianto & Muhtosim Arief & Mohammad Hamsal & Elidjen Elidjen, 2024. "Actuarial Risk Management Practices and Firm Performance: The Mediating Role of E-Service Innovation," JRFM, MDPI, vol. 17(5), pages 1-15, May.
    14. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    15. Daixin Wang & Zhiqiang Zhang & Yeyu Zhao & Kai Huang & Yulin Kang & Jun Zhou, 2024. "Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning," Papers 2403.06482, arXiv.org.
    16. Bermúdez, Ll. & Pérez, J.M. & Ayuso, M. & Gómez, E. & Vázquez, F.J., 2008. "A Bayesian dichotomous model with asymmetric link for fraud in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 779-786, April.
    17. Denisa BANULESCU-RADU & Meryem YANKOL-SCHALCK, 2021. "Fraud detection in the era of Machine Learning: a household insurance case," LEO Working Papers / DR LEO 2904, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

  6. Viaene, Stijn & Dedene, Guido, 2005. "Cost-sensitive learning and decision making revisited," European Journal of Operational Research, Elsevier, vol. 166(1), pages 212-220, October.

    Cited by:

    1. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    2. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    3. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    4. Haupt, Johannes & Bender, Benedict & Fabian, Benjamin & Lessmann, Stefan, 2018. "Robust identification of email tracking: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 271(1), pages 341-356.
    5. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    6. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
    7. Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021. "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers hal-02507499, HAL.
    8. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    9. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.

  7. Stijn Viaene & Guido Dedene, 2004. "Insurance Fraud: Issues and Challenges," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 29(2), pages 313-333, April.

    Cited by:

    1. Baumberg, Ben, 2016. "Benefit `myths'? The accuracy and inaccuracy of public beliefs about the benefits system," LSE Research Online Documents on Economics 103512, London School of Economics and Political Science, LSE Library.
    2. Reurink, Arjan, 2016. "Financial fraud: A literature review," MPIfG Discussion Paper 16/5, Max Planck Institute for the Study of Societies.
    3. Emmanuel Laffort & Nicolas Dufour, 2021. "Prise en compte de la fraude dans les organisations : comment libérer la parole ?," Post-Print hal-03336041, HAL.
    4. Sungkwol Park & Xiaoyong Zheng & Roderick M. Rejesus & Barry K. Goodwin, 2022. "Somebody's watching me! Impacts of the spot check list program in U.S. crop insurance," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 921-946, May.
    5. Engström, Per & Hesselius, Patrik, 2007. "The information method - theory and application," Working Paper Series 2007:17, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    6. Nils Mahlow & Joël Wagner, 2016. "Evolution of Strategic Levers in Insurance Claims Management: An Industry Survey," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 19(2), pages 197-223, September.
    7. Viaene, Stijn & Ayuso, Mercedes & Guillen, Montserrat & Van Gheel, Dirk & Dedene, Guido, 2007. "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, Elsevier, vol. 176(1), pages 565-583, January.
    8. Lu-Ming Tseng & Yue-Min Kang, 2015. "Managerial Authority, Turnover Intention and Medical Insurance Claims Adjusters’ Recommendations for Claim Payments," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 40(2), pages 334-352, April.
    9. Haithem Zourrig & Jeongsoo Park, 2019. "The effects of cultural tightness and perceived unfairness on Japanese consumers’ attitude towards insurance fraud: the mediating effect of rationalization," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(1), pages 21-30, June.
    10. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    11. Ming-Jyh Wang & Chieh-Hua Wen & Lawrence W Lan, 2010. "Modelling Different Types of Bundled Automobile Insurance Choice Behaviour: The Case of Taiwan*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 35(2), pages 290-308, April.
    12. Renee Flasher & Melvin A. Lamboy-Ruiz, 2019. "Impact of Enforcement on Healthcare Billing Fraud: Evidence from the USA," Journal of Business Ethics, Springer, vol. 157(1), pages 217-229, June.
    13. Pierre Picard, 2012. "Economic Analysis of Insurance Fraud," Working Papers hal-00725561, HAL.
    14. Jill M. Bisco & Kathleen A. McCullough & Charles M. Nyce, 2019. "Postclaim Underwriting And The Verification Of Insured Information: Evidence From The Life Insurance Industry," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 86(1), pages 7-38, March.
    15. Warren, Danielle E. & Schweitzer, Maurice E., 2021. "When weak sanctioning systems work: Evidence from auto insurance industry fraud investigations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 166(C), pages 68-83.
    16. Lu-Ming Tseng & Yue-Min Kang, 2014. "The influences of sales compensations, management stringency and ethical evaluations on product recommendations made by insurance brokers," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 22(1), pages 26-42, February.
    17. Lu-Ming Tseng & Wen-Pin Su, 2014. "Insurance Salespeople's Attitudes towards Collusion: The Case of Taiwan’s Car Insurance Industry," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 39(1), pages 25-41, January.
    18. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    19. Arash Rashidian & Hossein Joudaki & Taryn Vian, 2012. "No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
    20. Emmanuel Laffort & Nicolas Dufour, 2020. "External Fraud Risk Management seen from Luhmann’s Systemic Perspective and a Tentative Reading of Healthcare Insurance Companies’ Measures through this Perspective," Post-Print hal-03336033, HAL.

  8. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.

    Cited by:

    1. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    2. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    3. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
    4. Andreea Costea, 2017. "A Quantitative Approach to Credit Risk Management in the Underwriting Process for the Retail Portfolio," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 20(63), pages 157-186, March.
    5. Ali Namaki & Reza Eyvazloo & Shahin Ramtinnia, 2023. "A systematic review of early warning systems in finance," Papers 2310.00490, arXiv.org.
    6. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    7. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    8. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    9. Anna Stelzer, 2019. "Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions," Papers 1907.12996, arXiv.org.
    10. Hoffmann, F. & Baesens, B. & Mues, C. & Van Gestel, T. & Vanthienen, J., 2007. "Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 540-555, February.
    11. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    12. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    13. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    14. G. Verstraeten & D. Van Den Poel, 2006. "Using Predicted Outcome Stratified Sampling to Reduce the Variability in Predictive Performance of a One-Shot Train-and-Test Split for Individual Customer Predictions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/360, Ghent University, Faculty of Economics and Business Administration.
    15. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    16. Mark Schreiner, 2015. "A Comparison of Two Simple, Low-Cost Ways for Local, Pro-Poor Organizations to Measure the Poverty of Their Participants," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 537-569, November.
    17. Elena Ivona Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2022. "Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects," Post-Print hal-03331114, HAL.
    18. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    19. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    20. Dargnies, Marie-Pierre & Hakimov, Rustamdjan & Kübler, Dorothea, 2022. "Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence," Rationality and Competition Discussion Paper Series 334, CRC TRR 190 Rationality and Competition.
    21. Bravo, Cristián & Maldonado, Sebastián & Weber, Richard, 2013. "Granting and managing loans for micro-entrepreneurs: New developments and practical experiences," European Journal of Operational Research, Elsevier, vol. 227(2), pages 358-366.
    22. R Setiono & S-L Pan & M-H Hsieh & A Azcarraga, 2006. "Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(3), pages 231-240, March.
    23. Michalis Vafopoulos, 2011. "Looking for grass-root sources of systemic risk: the case of "cheques-as-collateral" network," Papers 1112.1156, arXiv.org.
    24. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    25. B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.
    26. Mireille Bardos, 2007. "What is at stake in the construction and use of credit scores?," Computational Economics, Springer;Society for Computational Economics, vol. 29(2), pages 159-172, March.
    27. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    28. Oguz Koc & Omur Ugur & A. Sevtap Kestel, 2023. "The Impact of Feature Selection and Transformation on Machine Learning Methods in Determining the Credit Scoring," Papers 2303.05427, arXiv.org.
    29. Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
    30. Adnan Dželihodžić & Dženana Đonko & Jasmin Kevrić, 2018. "Improved Credit Scoring Model Based on Bagging Neural Network," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1725-1741, November.
    31. Teply, Petr & Polena, Michal, 2020. "Best classification algorithms in peer-to-peer lending," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    32. Pawełek Barbara, 2019. "Extreme Gradient Boosting Method In The Prediction Of Company Bankruptcy," Statistics in Transition New Series, Statistics Poland, vol. 20(2), pages 155-171, June.
    33. Debaere, Steven & Coussement, Kristof & De Ruyck, Tom, 2018. "Multi-label classification of member participation in online innovation communities," European Journal of Operational Research, Elsevier, vol. 270(2), pages 761-774.
    34. S M Finlay, 2006. "Predictive models of expenditure and over-indebtedness for assessing the affordability of new consumer credit applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 655-669, June.
    35. Shian-Chang Huang & Cheng-Feng Wu & Chei-Chang Chiou & Meng-Chen Lin, 2022. "Intelligent FinTech Data Mining by Advanced Deep Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1407-1422, April.
    36. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
    37. S M Finlay, 2008. "Towards profitability: a utility approach to the credit scoring problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 921-931, July.
    38. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    39. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    40. Barbara Pawełek, 2019. "Extreme Gradient Boosting Method In The Prediction Of Company Bankruptcy," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 14(2), pages 155-171, June.
    41. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    42. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    43. Véronique Van Vlasselaer & Tina Eliassi-Rad & Leman Akoglu & Monique Snoeck & Bart Baesens, 2017. "GOTCHA! Network-Based Fraud Detection for Social Security Fraud," Management Science, INFORMS, vol. 63(9), pages 3090-3110, September.
    44. Neuberg Richard & Hannah Lauren, 2017. "Loan pricing under estimation risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 69-87, June.
    45. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    46. Christian Kurniawan & Xiyu Deng & Adhiraj Chakraborty & Assane Gueye & Niangjun Chen & Yorie Nakahira, 2022. "A Learning and Control Perspective for Microfinance," Papers 2207.12631, arXiv.org, revised Dec 2022.
    47. Surjaningsih, Ndari & Werdaningtyas, Hesti & Rahman, Faizal & Falaqh, Romadhon, 2022. "Predicting Household Resilience Before and During Pandemic with Classifier Algorithms," OSF Preprints w5q9g, Center for Open Science.
    48. Karel Dejaeger & Frank Goethals & Antonio Giangreco & Lapo Mola & Bart Baesens, 2012. "Gaining insight into student satisfaction using comprehensible data mining techniques," Post-Print halshs-01929190, HAL.
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    Cited by:

    1. Boonen, Tim J., 2016. "Nash equilibria of Over-The-Counter bargaining for insurance risk redistributions: The role of a regulator," European Journal of Operational Research, Elsevier, vol. 250(3), pages 955-965.
    2. Li Sanxi & Xiao Hao & Yao Dongmin, 2013. "Contract Bargaining with a Risk-Averse Agent," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 13(1), pages 285-301, November.
    3. Raduna, Daniela Viviana & Roman, Mihai Daniel, 2011. "Risk aversion influence on insurance market," MPRA Paper 37725, University Library of Munich, Germany, revised 01 Feb 2012.
    4. Zhou, Rui & Li, Johnny Siu-Hang & Tan, Ken Seng, 2015. "Modeling longevity risk transfers as Nash bargaining problems: Methodology and insights," Economic Modelling, Elsevier, vol. 51(C), pages 460-472.
    5. Quiggin, John & Chambers, Robert G., 2005. "Bargaining power and efficiency in insurance contracts," Risk and Sustainable Management Group Working Papers 151182, University of Queensland, School of Economics.
    6. Huang, Rachel J. & Huang, Yi-Chieh & Tzeng, Larry Y., 2013. "Insurance bargaining under ambiguity," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 812-820.
    7. Giuseppe Attanasi & Laura Concina & Caroline Kamaté & Valentina Rotondi, 2020. "Firm’s protection against disasters: are investment and insurance substitutes or complements?," Theory and Decision, Springer, vol. 88(1), pages 121-151, February.

  10. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.

    Cited by:

    1. Bilal Zorić, Alisa, 2015. "Case Study in Banking Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 251-257, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    2. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    3. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    4. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    5. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    6. Ma, Tiejun & Tang, Leilei & McGroarty, Frank & Sung, Ming-Chien & Johnson, Johnnie E. V, 2016. "Time is money: Costing the impact of duration misperception in market prices," European Journal of Operational Research, Elsevier, vol. 255(2), pages 397-410.
    7. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
    8. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
    9. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
    10. B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.
    11. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    12. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
    13. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.
    14. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
    15. Aslan Lotfi & Zhengrui Jiang & Ali Lotfi & Dipak C. Jain, 2023. "Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach," Information Systems Research, INFORMS, vol. 34(2), pages 409-422, June.
    16. Viaene, Stijn & Dedene, Guido, 2005. "Cost-sensitive learning and decision making revisited," European Journal of Operational Research, Elsevier, vol. 166(1), pages 212-220, October.
    17. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
    18. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    19. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    20. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
    21. Mihai TICHINDELEAN, 2013. "Models Used for Measuring Customer Engagement," Expert Journal of Marketing, Sprint Investify, vol. 1(1), pages 38-49.
    22. Jinping Hu, 2023. "Customer feature selection from high-dimensional bank direct marketing data for uplift modeling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 160-171, June.
    23. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    24. Seret, Alex & Verbraken, Thomas & Versailles, Sébastien & Baesens, Bart, 2012. "A new SOM-based method for profile generation: Theory and an application in direct marketing," European Journal of Operational Research, Elsevier, vol. 220(1), pages 199-209.
    25. Bert de Reyck & Zeger Degraeve, 2003. "Broadcast Scheduling for Mobile Advertising," Operations Research, INFORMS, vol. 51(4), pages 509-517, August.
    26. Alisa Bilal Zoric, 2016. "Predicting customer churn in banking industry using neural networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 14(2), pages 116-124.
    27. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    28. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
    29. B. Baesens & G. Verstraeten & D. Van Den Poel & M. Egmont-Petersen & P. Van Kenhove & J. Vanthienen, 2002. "Bayesian Network Classifiers for Identifying the Slope of the Customer - Lifecycle of Long-Life Customers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 02/154, Ghent University, Faculty of Economics and Business Administration.
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